A Reduced-Complexity Decentralized Positioning and Tracking Algorithm for Wireless Sensor Networks

In this paper, we propose a reduced-complexity decentralized positioning (RCDP) algorithm for target positioning and tracking in wireless sensor networks. The proposed approach formulates the receiver signal strength (RSS) estimation error as a nonlinear least squares (NLS) problem using the RSS information of the local sensor nodes, and then applies the RCDP algorithm iteratively to estimate the target location. In the localization process, a participating sensor node updates the target´s estimated location and then passes the updated estimate on to the next participating sensor node, which calculates a new estimated target location. We also perform a convergence analysis of the RCDP-based method based on the mathematical proofs to guarantee that the proposed iterative localization process converges. Computer simulation results show that our proposed method has a higher estimation accuracy and a better tracking efficiency than previous related methods in both stationary and moving target scenarios.